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Modern technologies are widely used today to diagnose epilepsy, neurological disorders, and brain tumors. Meanwhile, it is not cost-effective in terms of time and money to use a large amount of electroencephalography (EEG) data from different centers and collect them in a central server for processing and analysis. Collecting this data correctly is challenging, and organizations avoid sharing their and client information with others due to data privacy protection. It is difficult to collect these data correctly and it is challenging to transfer them to research centers due to the privacy of the data. In this regard, collaborative learning as an extraordinary approach in this field paves the way for the use of information repositories in research matters without transferring the original data to the centers. This study focuses on the use of a heterogeneous client balancing technique with an interval selection approach and classification of EEG signals with ResNet50 deep architecture. The test results achieved an accuracy of 99.14 compared to similar methods.

Categorization of cardiac abnormalities received from several centers is not possible within the quickest time because of privacy and security restrictions. Today, individuals’ security problem is considered as one of the most important research fields in most research sciences. This study provides a novel approach for detection of cardiac abnormalities based on federated learning (FL). This approach addresses the challenge of accessing data from remote centers and presents the possibility of learning without the need for transferring data from the main center. We present a novel aggregation approach in the FL for addressing the challenge of imbalanced data using the averaging stochastic weights (SWA) optimizer and a multivariate Gaussian in order to make a better and more accurate detection possible. The advantage of the present proposed approach is robust and secure aggregation for unbalanced electrocardiogram (ECG) data from heterogeneous clients. We were able to achieve 87.98% accuracy in testing with the robust VGG19 architecture.

Pedestrian safety is a serious problem in transportation systems because pedestrian and vehicle crashes often result in fatalities amongst vulnerable road users. A vehicle-to-pedestrian (V2P) communication system allows data exchange between pedestrians and vehicles to prevent or minimise potential dangers of accidents from happening. This work aimed to analyse and review the previous work associated with information exchange in the V2P communication system and classify the existing technology utilized for this purpose. Motivation, accessible problems confronting researchers, and suggestions posed to researchers to develop this critical area of study have been among the reasons considered to enhance awareness of the field's numerous qualitative facets in reported investigations and properties. All of the papers have been divided into four categories: growth, analysis, and survey, FRAMEWORK, and data exchange in the V2P communication system. V2P communication is an area that necessitates automated solutions, instruments, and techniques that allow pedestrian detection and prediction. Pedestrian identification and data sharing on V2P have been the subject of several experiments in order to support pedestrian protection techniques. The reasons, open barriers that hinder the technology's usefulness, and authors' suggestions have been used to identify the essential characteristics of this evolving sector. This study is intended to provide researchers with new resources and enable them to focus on the holes that have been found.